Search Results for author:
Found 10 papers, 6 papers with code
Learning a Fast 3D Spectral Approach to Object Segmentation and Tracking over Space and Time
Our motivation for a spectral space-time clustering approach, unique in video semantic segmentation literature, is that such clustering is dedicated to preserving object consistency over time, which we evaluate using our novel segmentation consistency measure.
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario.
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models.
Ranked #1 on
Unsupervised Anomaly Detection
on AnoShift
Rethinking the Authorship Verification Experimental Setups
One of the main drivers of the recent advances in authorship verification is the PAN large-scale authorship dataset.
DATE: Detecting Anomalies in Text via Self-Supervision of Transformers
Leveraging deep learning models for Anomaly Detection (AD) has seen widespread use in recent years due to superior performances over traditional methods.
Ranked #1 on
Anomaly Detection
on AG News
Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift
The human ability to synchronize the feedback from all their senses inspired recent works in multi-task and multi-modal learning.
SFTrack++: A Fast Learnable Spectral Segmentation Approach for Space-Time Consistent Tracking
We propose an object tracking method, SFTrack++, that smoothly learns to preserve the tracked object consistency over space and time dimensions by taking a spectral clustering approach over the graph of pixels from the video, using a fast 3D filtering formulation for finding the principal eigenvector of this graph's adjacency matrix.
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
Our method is based on the power iteration for finding the principal eigenvector of a matrix, which we prove is equivalent to performing a specific set of 3D convolutions in the space-time feature volume.
Ranked #41 on
Semi-Supervised Video Object Segmentation
on DAVIS 2016
(Jaccard (Mean) metric)
Learning a Robust Society of Tracking Parts using Co-occurrence Constraints
We address this challenge by proposing a deep neural network composed of different parts, which functions as a society of tracking parts.
Learning a Robust Society of Tracking Parts
They are classifiers that respond at different scales and locations.
